MeTTa-NARS (Non-Axiomatic Reasoning System)

MeTTa-NARS is an open-ended uncertainty reasoning engine designed to operate under the Assumption of Insufficient Knowledge and Resources (AIKR). Unlike traditional logical systems that require complete, clean data to function, MeTTa-NARS is built for the open world where information is scarce, inconsistent, and constantly changing. It provides real-time adaptive intelligence capable of learning logical dependencies and making decisions based on incomplete evidence, rather than waiting for absolute certainty.

The system distinguishes itself through its usage of Non-Axiomatic Logic (NAL), which replaces binary truth with a two-dimensional evidence value (frequency and confidence). This allows the agent to distinguish between “I know this is true because I have seen it 100 times” and “I think this is true, but I have only seen it once”—a critical nuance for safe autonomous learning. MeTTa-NARS manages this knowledge via a concept-centric memory and a rigorous inference control mechanism. This controller treats reasoning as a resource allocation problem, dynamically prioritizing relevant thoughts and discarding less useful information to ensure the system remains responsive in real-time without suffering from combinatorial explosions.

MeTTa-NARS drives the active, never-ending learning loop, allowing agents to continually refine their understanding of the world as they encounter new, unexpected phenomena.